In conventional wireless communications, a single antenna is used at a source to wirelessly transmit a signal to a single antenna at a destination. In some cases, this gives rise to problems due to multipath effects. For example, when a wireless signal (an electromagnetic field) is met with obstructions such as hills, canyons, buildings, and utility wires, wavefronts are scattered, and thus may take many paths to reach the destination. Late arrival of scattered portions of the signal causes problems such as fading, cut-out, and intermittent reception.
Multiple input, multiple output (MIMO) is an antenna technology for wireless communications in which multiple antennas are used at both the source (transmitter) and the destination (receiver). The antennas at each end of the communication are combined to minimize errors and optimize data speed. MIMO is one of several forms of smart antenna technology. Other forms include multiple input, single output (MISO) and single input, multiple output (SIMO). Using smart antenna technology (e.g., multiple antennas at both the source and the destination) can eliminate the signal problems caused by multipath wave propagation, and can even take advantage of this effect.
Availability of channel status information (CSI) at a transmitter allows signal processing to be carried out at the transmitter in multiuser systems. Such pre-processing of signals before transmission, for instance, can achieve user interference suppression at the transmitter. For example, in a downlink multiuser MISO system (where a base station (BS) equipped with multiple transmit antennas sends data to multiple downlink users that each have one receive antenna), with the knowledge of both the channel matrix (e.g., obtained through feedback from receivers) as well as information symbols of all the users, the BS can perform “precoding” on the information symbol vector. Precoding on signals provides the ability to remove unwanted other-user signal interference at a desired user terminal, which allows the user terminal receiver to be less complex. Such pre-processing of signals has been described as dirty paper coding (DPC), which, in Gaussian broadcast multiuser MIMO channels, has been shown to theoretically achieve sum capacity (i.e., maximum aggregation of all users' data rates) that grows linearly with a minimum number of BS antennas (Nt) and number of users (Nu) provided that the transmitter and receivers all have knowledge regarding the channel.
However, practical pre-processing techniques that aim to achieve the sum capacity as promised by DPC are challenging. Linear precoders including normalized zero-forcing (ZF) and minimum mean square error (MMSE) precoders, and non-linear precoders including Tomlinson-Harashima precoders (THP) have relatively little complexity, but generally do not achieve full diversity in the system. Alternatively, other precoders based on vector perturbation and several other variants have been shown to achieve good performance at the expense of increased complexity.